code: def test_distributed_tensor_gatherer_different_shapes(self):
      # Simulate a result with a dataset of size 21, 4 processes and chunks of lengths 2, 3, 1
      world_size = 4
      num_samples = 21
      input_indices = [
          [0, 1, 6, 7, 12, 13, 18, 19],
          [2, 3, 4, 8, 9, 10, 14, 15, 16, 20, 0, 1],
          [5, 11, 17, 2],
      ]
      sequence_lengths = [8, 10, 13]
  
      predictions = np.random.normal(size=(num_samples, 13))
      gatherer = DistributedTensorGatherer(
          world_size=world_size, num_samples=num_samples
      )
      for indices, seq_length in zip(input_indices, sequence_lengths):
          gatherer.add_arrays(predictions[indices, :seq_length])
      result = gatherer.finalize()
  
      # Remove the extra samples added at the end for a round multiple of num processes.
      actual_indices = [
          input_indices[0],
          input_indices[1][:-2],
          input_indices[2][:-1],
      ]
      for indices, seq_length in zip(actual_indices, sequence_lengths):
          self.assertTrue(
              np.array_equal(
                  result[indices, :seq_length],
                  predictions[indices, :seq_length],
              )
          )
  
      # With nested tensors
      predictions = np.random.normal(size=(num_samples, 13))
      gatherer = DistributedTensorGatherer(
          world_size=world_size, num_samples=num_samples
      )
      for indices, seq_length in zip(input_indices, sequence_lengths):
          gatherer.add_arrays(
              [predictions[indices, :seq_length], predictions[indices]]
          )
      result = gatherer.finalize()
  
      for indices, seq_length in zip(actual_indices, sequence_lengths):
          self.assertTrue(
              np.array_equal(
                  result[0][indices, :seq_length],
                  predictions[indices, :seq_length],
              )
          )
      self.assertTrue(np.array_equal(result[1], predictions))
  
      # Check if works if varying seq_length is second
      gatherer = DistributedTensorGatherer(
          world_size=world_size, num_samples=num_samples
      )
      for indices, seq_length in zip(input_indices, sequence_lengths):
          gatherer.add_arrays(
              [predictions[indices], predictions[indices, :seq_length]]
          )
      result = gatherer.finalize()
  
      self.assertTrue(np.array_equal(result[0], predictions))
      for indices, seq_length in zip(actual_indices, sequence_lengths):
          self.assertTrue(
              np.array_equal(
                  result[1][indices, :seq_length],
                  predictions[indices, :seq_length],
              )
          )
  
imports: import copy
  import unittest
  import numpy
  from transformers.data.data_collator import default_data_collator
  from transformers.testing_utils import require_accelerate, require_torch
  from transformers.trainer_utils import RemoveColumnsCollator, find_executable_batch_size
  from transformers.utils import is_torch_available
label_desc: Use image node_id 2 for calling the TrainerUtilsTest obj's underlying member method code with example usage: obj.test_distributed_tensor_gatherer_different_shapes() without return types